Three-dimensional Variational Scheme Research Articles

Assimilation of high-resolution sea surface temperature data into an operational nowcast/forecast system around Japan using a multi-scale three-dimensional variational scheme

A multi-scale three-dimensional variational (MS-3DVAR) scheme is developed to assimilate high-resolution Himawari-8 sea surface temperature (SST) data for the first time into an operational ocean nowcast/forecast system targeting the North Western Pacific, JCOPE2. MS-3DVAR improves representation of the Kuroshio path south of Japan, its associated sea level variations, and temperature/salinity profiles south of Japan, the Kuroshio/Oyashio mixed water region, and the Japan Sea as compared to those of the products by the traditional single-scale 3DVAR. Validation results demonstrate that MS-3DVAR well assimilates the sparsely distributed in situ temperature and salinity profiles data by spreading the information over the large scale and by representing the detailed information near the measurement points. MS-3DVAR succeeds to assimilate the Himawari-8 SST product without noisy features caused by the cloud effects. We also find that MS-3DVAR is more effective for estimating oceanic conditions in regions with smaller mesoscale variability including the mixed water region and Japan Sea than in south of Japan.

Multi-species chemical data assimilation with the Danish Eulerian hemispheric model: system description and verification

Satellite retrievals of atmospheric composition provide a wealth of data on a global scale. These complement results from atmospheric chemistry-transport models (CTMs), and can be combined using data assimilation. We present two assimilation schemes coupled to the Danish Eulerian Hemispheric Model (DEHM), a three-dimensional, off-line CTM with full photochemistry: a variant on the ensemble Kalman filter and the three-dimensional variational scheme. The aim of this paper is to describe the two schemes and present an initial assessment of their impacts on model skill. Retrievals of multiple atmospheric trace gases are assimilated, namely: NO2 tropospheric column densities, CH4 total column densities, and partial column concentrations of O3, CO and CH4; these data are retrieved from four satellite sensors. Data for each species are assimilated independently of one another, and other species are only adjusted indirectly via the model’s chemistry and dynamics. Assimilation results are compared with measurements from surface monitoring stations and other satellite retrievals, and preliminary validation results are presented.Reference simulations (without assimilation) grossly underestimate surface CO concentrations, and both assimilation schemes eliminate this large and systematic model bias. The assimilation improves the spatial correlation of modelled CO with surface observations, and improves the spatial correlation between forecasts and retrievals for CO, NO2 and O3. Results for CH4 show a loss of skill due to a mismatch in model bias between two assimilated CH4 data-sets. Finally, we discuss differences in methodology and results between this paper and a recent study on multi-species chemical data assimilation. Joint optimisation of initial conditions and emission rates offers a promising direction for improving modelled boundary-layer concentrations.

Coastal ocean data assimilation using a multi-scale three-dimensional variational scheme

A multi-scale three-dimensional variational scheme (MS-3DVAR) is implemented to improve the effectiveness of the assimilation of both very sparse and high-resolution observations into models with resolutions down to 1 km. The improvements are realized through the use of background error covariances of multi-decorrelation length scales and by reducing the inherent observational representativeness errors. MS-3DVAR is applied to coastal ocean data assimilation to handle the wide range of spatial scales that exist in both the dynamics and observations. In the implementation presented here, the cost function consists of two components for large and small scales, and MS-3DVAR is implemented sequentially from large to small scales. A set of observing system simulation experiments (OSSEs) are performed to illustrate the advantages of MS-3DVAR over conventional 3DVAR in assimilating two of the most common types of observations—sparse vertical profiles and high-resolution surface measurements—simultaneously. One month of results from an operational implementation show that both the analysis error and bias are reduced more effectively when using MS-3DVAR.

Quality Assessment of a 1985–2007 Mediterranean Sea Reanalysis

Abstract A simulation and two reanalyses from 1985 to 2007 have been produced for the Mediterranean Sea using different assimilation schemes: a reduced-order optimal interpolation [System for Ocean Forecast and Analysis (SOFA)] and a three-dimensional variational scheme (OceanVar). The observational dataset consists of vertical temperature and salinity in situ profiles and along-track satellite sea level anomalies; daily mean fields of satellite sea surface temperature are used for correcting the air–sea fluxes. This paper assesses the quality of the reanalyses with respect to observations and the simulation. Both the SOFA and OceanVar schemes give very similar root-mean-square errors and biases for temperature and salinity fields compared with the assimilated observations. The largest errors are at the thermocline level and in regions of large eddy field variability. However, OceanVar gives 20% better results for sea level anomaly root-mean-square error.

Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.

Assimilation of HF radar data into the SWAN wave model

Data assimilation is a method used for optimally combining information to improve forecasting and model parameters. Three data assimilation algorithms are investigated and used for assimilating HF radar data into the SWAN wave model. The schemes under consideration are the Ensemble Kalman Filter, Ensemble Optimal Interpolation and a Three-Dimensional Variational scheme. Two approaches for updating the ocean wave spectrum are considered which consist of describing the spectrum using integral wave parameters of the whole spectrum and wave parameters within various frequency bands. The results show improvement in the wave parameters at the buoy location for the 3DVAR and ENS-OI scheme. The EnKF only shows good performance in one of the considered runs which leads to a conclusion that the methods used for calculating the errors for the EnKF scheme need further analysis.